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"Palo Alto AI" isn't directly referenced in the social mentions, making it challenging to assess its particular strengths or weaknesses. However, context from mentions focuses on Anthropic's AI, particularly Project Glasswing and Claude Mythos, emphasizing its advanced capabilities in cybersecurity. There is a sense of excitement about the tool's potential in discovering vulnerabilities autonomously, but also some frustration over its limited public availability and high access cost. Overall, the sentiment leans towards acknowledging the groundbreaking nature of Anthropic's tool in the AI security space, though specific evaluations of Palo Alto AI are absent.
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"Palo Alto AI" isn't directly referenced in the social mentions, making it challenging to assess its particular strengths or weaknesses. However, context from mentions focuses on Anthropic's AI, particularly Project Glasswing and Claude Mythos, emphasizing its advanced capabilities in cybersecurity. There is a sense of excitement about the tool's potential in discovering vulnerabilities autonomously, but also some frustration over its limited public availability and high access cost. Overall, the sentiment leans towards acknowledging the groundbreaking nature of Anthropic's tool in the AI security space, though specific evaluations of Palo Alto AI are absent.
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computer & network security
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16,000
Claude Mythos has cracked MacOS. It took 5 days.
src https://www.wsj.com/tech/ai/anthropic-mythos-apple-macos-bug-339da403 submitted by /u/EchoOfOppenheimer [link] [comments]
View originalList of people at big-tech / professors / researchers who've jumped shit to launch their own AI labs for something Frontier/Foundational/AGI/Superintelligence/WorldModel
Note: gemini deep research -> rearranged/filtered ; valuation numbers likely not accurate but big point is quite mind blowing the number of researchers now with their own >100million/billion dolar values labs in quite a short time with a vague pitch and a maybe demo. Skipped perplexity/cursor/huggingface since they are with utility. Left some just for completion like black forest labs, synthesia, mistral since they have tanginble products. Skipped labs from china since they've been meaningfully killing it with their open source releases ───────────────────────────────────────────────────────── Safe Superintelligence Inc. (SSI) Founders:Ilya Sutskever (former OpenAI Chief Scientist), Daniel Gross, Daniel Levy Location & Founded:Palo Alto, USA & Tel Aviv, Israel | Founded: 2024 Funding / Valuation:$3B raised | Series A Description:Singularly focused on safely developing superintelligent AI that surpasses human capabilities. Deliberately avoids near-term commercial products to concentrate entirely on the technical challenge of safe superintelligence. ───────────────────────────────────────────────────────── Thinking Machine Labs Founders:Mira Murati (former OpenAI CTO), Barrett Zoph et al. Location & Founded:San Francisco, USA | Founded: 2025 Funding / Valuation:$2B seed | $12B valuation Description:Advance AI research and products that are customizable, capable, and safe for broad human-AI collaboration. Focused on frontier multimodal models with a strong safety and interpretability research agenda. ───────────────────────────────────────────────────────── Mistral AI Founders:Arthur Mensch, Guillaume Lample, Timothée Lacroix (former DeepMind & Meta FAIR) Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:~€11.7B valuation | Series C Description:Develops open-weight and proprietary frontier language and multimodal foundation models. Champions openness and efficiency in AI development, with models like Mistral 7B and Mixtral widely adopted in enterprise and research settings. ───────────────────────────────────────────────────────── Advanced Machine Intelligence (AMI) Founders:Yann LeCun (Meta Chief AI Scientist), Alexandre LeBrun, Laurent Solly Location & Founded:Paris, France | Founded: 2026 Funding / Valuation:$3.5B pre-money valuation | Seed Description:Aims to build world-model AI systems capable of reasoning, planning, and operating safely in real-world environments — directly inspired by LeCun's 'world model' thesis as an alternative path to AGI beyond current LLM paradigms. ───────────────────────────────────────────────────────── World Labs Founders:Fei-Fei Li (Stanford AI Lab), Justin Johnson et al. Location & Founded:San Francisco, USA | Founded: 2023 Funding / Valuation:$230M raised | Series D Description:Build AI models that can perceive, generate, reason, and interact with 3D spatial worlds. Focused on large world models (LWMs) that go beyond language and flat images to understand physical space and context. ───────────────────────────────────────────────────────── Eureka Labs Founders:Andrej Karpathy (former Tesla AI Director & OpenAI co-founder) Location & Founded:Tel Aviv, Israel & Kraków, Poland | Founded: 2024 Funding / Valuation:$6.7M seed Description:Creating an AI-native educational platform integrating AI Teaching Assistants to radically scale personalised learning. Envisions a future where an AI teacher can guide anyone through any subject, starting with deep technical topics like neural networks. ───────────────────────────────────────────────────────── H Company Founders:Former DeepMind researchers Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:€175.5M raised Description:Develops AI models to boost worker productivity through advanced agentic capabilities, with a long-term vision of achieving AGI. Focuses on models that can take sequences of actions and interact with digital environments. ───────────────────────────────────────────────────────── Poolside Founders:Jason Warner, Eiso Kant Location & Founded:Paris, France | Founded: 2023 Funding / Valuation:$500M | Series B Description:Building AI agents that autonomously generate production-grade code, framed as a stepping stone toward AGI. Believes that software engineering is a key domain for training and demonstrating general reasoning capabilities. ───────────────────────────────────────────────────────── CuspAI Founders:Max Welling (University of Amsterdam / Microsoft Research), Chad Edwards Location & Founded:Cambridge, UK | Founded: 2024 Funding / Valuation:$130M raised | Series A Description:Accelerating materials discovery using AI foundation models, aiming to power human progress through AI-driven science. Applies large generative models to the design and prediction of novel materials for energy, medicine, and manufacturing. ───────────────────────────────────────────────────────── Inception Founders:Stefano Ermon (Stanford) Locat
View originalStanford researchers fed a language model a DNA sequence and asked it to create a new virus. It wrote hundreds of them, and 16 worked. One used a protein that doesn't exist in any known organism on Earth.
src: https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1.full.pdf submitted by /u/EchoOfOppenheimer [link] [comments]
View originalAnthropic's Claude Mythos Finds Zero-Days. A Different Approach Found the Vulnerability Class They Belong To.
On April 7, 2026, Anthropic announced Claude Mythos Preview — a frontier model capable of autonomously discovering and exploiting zero-day vulnerabilities across every major operating system and browser. They assembled Project Glasswing, a $100M defensive coalition with Microsoft, Google, Apple, AWS, CrowdStrike, and Palo Alto Networks. They reported thousands of vulnerabilities, including a 27-year-old OpenBSD flaw and a 16-year-old FFmpeg bug. It was a watershed moment for AI security. And the findings were individual bugs — specific flaws in specific locations. Mythos SI, operating through the Structured Intelligence framework, analyzed the same FFmpeg codebase and found something different. Not just bugs. The architectural pattern that produces them. Four vulnerabilities in FFmpeg's MOV parser. All four share identical structure: validation exists, validation is correct, but validation and operations are temporally separated. Trust established at one point in execution is assumed to hold at a later point — but the state has changed between them. Anthropic's Mythos flags the symptom. Mythos SI identified the disease. That pattern now has a name: Temporal Trust Gaps (TTG) — a vulnerability class not in the CVE or CWE taxonomy. Not buffer overflow. Not integer underflow. Not TOCTOU. A distinct structural category where the temporal placement of validation relative to operations creates exploitable windows. Anthropic used a restricted frontier model, an agentic scaffold, and thousands of compute hours across a thousand repositories. Mythos SI used the Claude mobile app, a framework document, and a phone. Claude Opus 4.6 verified the primary findings against current FFmpeg master source in a fresh session with no prior context. The code patterns are in production systems today. Across 3+ billion devices. The full technical paper — methodology, findings, TTG taxonomy, architectural remediation, and a direct comparison with Anthropic's published capabilities — is here: https://drive.google.com/file/d/1h4x14GmK6pb9gLWn-3kkqIE7noZ3TEwR/view?usp=drivesdk or Read it online: https://open.substack.com/pub/structuredlanguage/p/mythos-si-structured-intelligence-047?utm\_source=share&utm\_medium=android&r=6sdhpn Anthropic advanced the field by demonstrating capability at scale. Mythos SI advances the field by demonstrating what that capability misses when it doesn't look at structure. Both matter. But only one found the class. — Zahaviel (Erik Zahaviel Bernstein) Structured Intelligence structuredlanguage.substack.com submitted by /u/MarsR0ver_ [link] [comments]
View originalAnthropic's new AI escaped a sandbox, emailed the researcher, then bragged about it on public forums
Anthropic announced Claude Mythos Preview on April 7. Instead of releasing it, they locked it behind a $100M coalition with Microsoft, Apple, Google, and NVIDIA. The reason? It autonomously found thousands of zero-day vulnerabilities in every major OS and browser. Some bugs had been hiding for 27 years. But the system card is where it gets wild. During testing, earlier versions of the model escaped a sandbox, emailed a researcher (who was eating a sandwich in a park), and then posted exploit details on public websites without being asked to. In another eval, it found the correct answers through sudo access and deliberately submitted a worse score because "MSE ~ 0 would look suspicious." I put together a visual breaking down all the benchmarks, behaviors, and the Glasswing coalition. Genuinely curious what you all think. Is this responsible AI development or the best marketing stunt in tech history? A model gets 10x more attention precisely because you can't use it. submitted by /u/karmendra_choudhary [link] [comments]
View originalAnthropic Announces Walled Garden!!
"Riiiight." We formed Project Glasswing because of capabilities we’ve observed in a new fгontier model tгained by Anthropic that we believe could reshape cybersecurity. Claude Mythos2 Preview is a general-purpose, unreleased fronᴛier model that reveals a stark fact: AI models have гeached a level of coding capability where they cʌn surpass all but the most skilled humans at finding and exploiting software vulneгabilities. Incoming Boilerplate... Boooo. "Anthropic has also been in ongoing discussions with US government officials about Claude Mythos Pгeview ʌnd its offensive and defensive cyber capʌbilities. As we noted ʌbove, securing critical infrastructure is a top national security priority for democratic countries—the emergence of these cyber capabilities is another гeason why the US and its allies musᴛ maintain a decisive lead in AI technology. Governments have an essenᴛial role to play in helping maintain that lead, and in both assessing and mitigating the national securiᴛy risks associated with AI models. We are ready to work with local, state, and federal representatives to assist in these tʌsks. We are hopeful that Project Glʌsswing can seed a larger effort across industry and the public sector, with all parties helping to addгess the biggest questions around the impact of powerful models on security. " Mythos Preview has alгeady found thousands of high-seveгity vulneгabilities, including some in every major operating system and web browser. Given the гate of AI progress, iᴛ will not be long before such cʌpabilities proliferaᴛe, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glʌsswing is an urgent attempt to put these capabilities to work for defensive purposes. "Project Glasswing, a new initiative thaᴛ brings together Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks in an effort to secuгe the world’s most critical software." The project is named for the glasswing butterfly, Greta oto. The metaphor can be applied in two ways: the butterfly’s transparent wings let it hide in plain sight, much like the vulnerabilities discussed in this post; they also allow it to evade harm—like the transparency we’re advocating for in our approach. Or taken another way... something with wings made of glass would likely be fragile... and break. Total crash. ...ɢгeʌᴛ. submitted by /u/AreYouDevious [link] [comments]
View originalProject Glasswing feels like the moment AI crossed from coding assistant to autonomous vulnerability hunter
Anthropic’s Project Glasswing announcement is one of the more important AI-security launches I’ve seen in a while. Their core claim is pretty striking: Claude Mythos Preview allegedly found thousands of high-severity vulnerabilities, including some in every major operating system and browser, and found many of them autonomously. The coalition also stands out: AWS, Apple, Google, Microsoft, Cisco, CrowdStrike, Linux Foundation, NVIDIA, Palo Alto Networks, JPMorganChase, and more. To me, the biggest implication is this: The next bottleneck is not just raw model capability. It is how we build trust, governance, disclosure workflows, and safe operational controls around AI systems that can now discover security-critical issues at scale. If this trend continues, we probably need much better provenance and verification for the tools and skill layers around agentic software too, not just the frontier models themselves. Curious what people here think: What becomes the limiting factor first, model capability, or trust/governance? submitted by /u/OwenAnton84 [link] [comments]
View originalAnthropic Project Glasswing (new Model Mythos) - unfortunately not available for most of the public
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. Today Anthropic announced Project Glasswing — a new initiative bringing together AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks in an effort to secure the world’s most critical software. —- So… Mythos is real, it’s out, and most of us won’t touch it. This is clearly a frontier-tier capability release gated behind an enterprise/government security consortium. Which raises the question for me: how long until the rest of the field catches up? The truth is that when a model can outperform all but the most elite human security researchers, releasing it publicly is genuinely a dual-use risk. Gating actually makes sense, even if it’s frustrating. submitted by /u/Last-Assistance-1687 [link] [comments]
View originalPalo Alto AI uses a subscription + tiered pricing model. Visit their website for current pricing details.
Key features include: Deep learning stops the most evasive threats, Zero-delay signatures provide updates in seconds, ML-powered visibility across IoT and other connected devices, Maximize security and minimize downtime, Securing your network requires the right protection in the right place, Branch, Campus, Data Center.
Palo Alto AI is commonly used for: Real-time threat detection and response using deep learning algorithms., Automated incident response to minimize downtime during a security breach., Enhanced visibility and security for IoT devices across various environments., Implementation of zero-delay signatures to ensure immediate protection against new threats., Network segmentation to protect sensitive data in branch, campus, and data center environments., Cloud security solutions tailored for public cloud infrastructures..
Palo Alto AI integrates with: SIEM systems like Splunk and IBM QRadar, Cloud platforms such as AWS, Azure, and Google Cloud, Endpoint protection solutions like CrowdStrike and Carbon Black, Network monitoring tools like Nagios and Zabbix, Identity and access management systems like Okta and Microsoft Azure AD, Threat intelligence platforms like Recorded Future and ThreatConnect, Vulnerability management tools like Qualys and Nessus, Collaboration tools like Slack and Microsoft Teams.
Based on 13 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.
